How to Install and Uninstall r-cran-surveillance Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 22,2024

1. Install "r-cran-surveillance" package

Please follow the step by step instructions below to install r-cran-surveillance on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-surveillance
            
                    
    
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2. Uninstall "r-cran-surveillance" package

Please follow the guidance below to uninstall r-cran-surveillance on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-surveillance
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-surveillance package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-surveillance
Architecture: amd64
Version: 1.19.0-2
Priority: optional
Section: multiverse/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 6885
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-sp (>= 1.0-15), r-cran-xtable (>= 1.7-0), r-cran-rcpp (>= 0.11.1), r-cran-polycub (>= 0.8.0), r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-spatstat.geom, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2)
Recommends: r-cran-tinytest (>= 1.2.4), r-cran-spdep, r-cran-memoise, r-cran-maxlik, r-cran-numderiv
Suggests: r-cran-gridextra (>= 2.0.0), r-cran-lattice, r-cran-colorspace, r-cran-scales, r-cran-animation, r-cran-msm, r-cran-spc, r-cran-quadprog, r-cran-polyclip, r-cran-maptools, r-cran-intervals, r-cran-gsl, r-cran-coda, r-cran-knitr
Filename: pool/multiverse/r/r-cran-surveillance/r-cran-surveillance_1.19.0-2_amd64.deb
Size: 5790604
MD5sum: 15b711c670cbcbfdd40ee11b614fdb30
SHA1: da7e2fc5f4641fbdab880b8886f04140641775d6
SHA256: 3ed71e0642e1b1c94d98489ae398cbbfa688eb57bde7e554c1034e44b9ce56e6
SHA512: 1351acb96e2ae93e6d965eb13baa7602827a9239707b15dd25718d1e6e839e78ef51019b1772d2b9296aed6e5ffc0de6a4e3bfa9632b62df8008730d12f87ab3
Homepage: https://cran.r-project.org/package=surveillance
Description-en: GNU R package for the Modeling and Monitoring of Epidemic Phenomena
Statistical methods for the modeling and monitoring of time series of
counts, proportions and categorical data, as well as for the modeling of
continuous-time point processes of epidemic phenomena.
.
The monitoring methods focus on aberration detection in count data time
series from public health surveillance of communicable diseases, but
applications could just as well originate from environmetrics,
reliability engineering, econometrics, or social sciences. The package
implements many typical outbreak detection procedures such as the
(improved) Farrington algorithm, or the negative binomial GLR-CUSUM
method of Höhle and Paul (2008) . A novel
CUSUM approach combining logistic and multinomial logistic modeling is
also included. The package contains several real-world data sets, the
ability to simulate outbreak data, and to visualize the results of the
monitoring in a temporal, spatial or spatio-temporal fashion. A recent
overview of the available monitoring procedures is given by Salmon et al.
(2016) .
.
For the retrospective analysis of epidemic spread, the package provides
three endemic-epidemic modeling frameworks with tools for visualization,
likelihood inference, and simulation. hhh4() estimates models for
(multivariate) count time series following Paul and Held (2011)
and Meyer and Held (2014)
. twinSIR() models the
susceptible-infectious-recovered (SIR) event history of a fixed
population, e.g, epidemics across farms or networks, as a multivariate
point process as proposed by Höhle (2009) .
twinstim() estimates self-exciting point process models for a
spatio-temporal point pattern of infective events, e.g., time-stamped
geo-referenced surveillance data, as proposed by Meyer et al. (2012)
. A recent overview of the
implemented space-time modeling frameworks for epidemic phenomena is
given by Meyer et al. (2017) .
Description-md5: c0b0bb231313eb8bbfdc0a5bb8199755